Uneven Growth in China and India

Shubham Chaudhuri and Martin Ravallion*

World Bank, 1818 H Street NW, Washington DC

Abstract: The paper reviews evidence on the ways in which recent economic growth has been uneven in China and India and what this has meant for inequality and poverty. Drawing on analyses based on household survey data and aggregate data from official sources, we show that growth has indeed been uneven—geographically, sectorally and at the household-level—and that this has meant uneven progress against poverty, less poverty reduction than might have been achieved had growth been more balanced, and an increase in income inequality. The paper then examines why growth was uneven and why this should be of concern. The discussion is structured around the idea that there are both “good” and “bad” inequalities—drivers and dimensions of inequality and uneven growth that are good or bad in terms of what they imply for both equity and long-term growth and development. We argue that policies are needed that preserve the good inequalities—continued incentives for innovation and investment—but reduce the scope for bad ones, notably through investments in human capital and rural infrastructure that help the poor connect to markets.

* This is an updated and revised version of “Partially Awakened Giants: Uneven Growth in China and India,” which appeared in a published report of the World Bank, “Dancing with Giants: China, India, and the Global Economy,” edited by L. Alan Winters and Shahid Yusuf. These are the views of the authors, and should not be attributed to the World Bank. For their comments, the authors are grateful to Richard Cooper, David Dollar, Assar Lindbeck, Branko Milanovic, T.N. Srinivasan, Nicolas van de Walle, Alan Winters and participants at the Dragon and the Elephant Conference, Shanghai, 2006.

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1. Introduction

Economic growth in China and India since the 1980s has been accompanied by

substantial—in the case of China, dramatic—reductions in the aggregate incidence of absolute

poverty measured in terms of income or consumption. Figure 1 displays the trends for the two

countries over the period from 1981 to 2001. The headcount rates of poverty are calculated on as

comparable a basis as is currently feasible with the data available. The poverty line is the World

Bank’s dollar-a-day global standard of about $32.74 per month at 1993 Purchasing Power Parity.

China started this period with the higher poverty rate, but this soon changed.

However, concerns are being expressed about the distributional impacts of the growth

processes in both countries. The domestic debate about growth-promoting reforms has become

increasingly contentious. It is widely felt that the gains from growth have been spread too

unevenly, with some segments of the population left behind in relative and even absolute terms.

This unevenness has shown up as rising income inequality by conventional measures in both

countries. These developments in turn have led some to question the sustainability of growth.

What is one to make of this? In what ways has growth been uneven? Are the data

suggesting rising inequality to be believed? If so, should the fact that segments of the population

appear to have been left behind be of concern? And does this pose a risk to the sustainability of

growth and poverty reduction?

After noting a number of data issues, we review evidence on the ways in which growth

has been uneven in China and India and what that has meant for inequality and poverty. Drawing

on analyses based on existing household survey data and aggregate data from official sources,

we show that growth has indeed been uneven—geographically, sectorally and at the household-

level—and that this has meant uneven progress against poverty, less poverty reduction than

might have been achieved had growth been more balanced, and an increase in income inequality.

We then turn to why growth was uneven and why this should be of concern. Here, we draw on

the evidence that is available. But because of the complexity of the underlying issues and the

difficulties of settling them in an empirically rigorous manner, the discussion is necessarily

somewhat more speculative. We structure the discussion around the idea that there are both

“good” and “bad” inequalities—drivers and dimensions of inequality and uneven growth that are

good or bad in terms of what they imply for both equity and long-term growth and development.

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We argue that the development paths of both India and China have been influenced by,

and have generated, both types of inequalities and that while good inequalities—most notably

those that reflect the role of economic incentives—have been critical to the growth experience

thus far, there is a risk that bad inequalities—those that prevent individuals from connecting to

markets and limit investment and accumulation of human capital and physical capital—may

undermine the sustainability of growth in the coming years. We argue that policies are needed

that preserve the good inequalities—continued incentives for innovation and investment—but

reduce the scope for bad ones, notably through investments in human capital and rural

infrastructure that help the poor connect to markets.

2. Data issues

There are always reasons to be skeptical about economic statistics and measures of

inequality and poverty are no exceptions. The issues are rather different in these two countries.

A number of data problems have clouded past assessments of what has been happening to

poverty and inequality in China. Some of these problems are common to other countries

(developing and developed) while others are seemingly unique to China. Comparability between

urban and rural areas is a greater problem in China where the National Bureau of Statistics

(NBS) uses different survey instruments for urban and rural areas (whereas it is a unified survey

instrument in India, as elsewhere). The two nationally representative annual surveys for China

that we will draw on are the annual Rural Household Survey (RHS) and the annual Urban

Household Survey (UHS).

For the RHS there are also comparability problems over time, as discussed in Ravallion

and Chen (2007). One of the more serious problems is that there was a change in valuation

methods for consumption of own-farm production in the RHS in 1990 when public procurement

prices (held below market prices) were replaced by local selling prices.1 For 1990 (the only year

for which the two methods can be compared), Ravallion and Chen (2007) show that the new

valuation method generates slightly lower inequality; for 1990 the aggregate Gini index for rural

China drops from 31.5 percent to 29.9 percent; the rural headcount index of poverty drops

1 Until the mid-1990s, public procurement prices for grain were held below market prices. Using these prices to value own consumption over-estimates the true extent of both poverty and inequality. This practice was largely abandoned from 1990s onwards in favor of using local selling prices for valuation.

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substantially, from 37.6 percent to 29.9 percent. This reflects the high share of consumption from

own-farm product among China’s poor.

Another problem in past work has been the failure to adjust for spatial cost-of-living

differences. This can affect distributional comparisons over space and time. The extent of urban–

rural disparities drops appreciably once one corrects for the fact that the urban cost of living is

higher (Ravallion and Chen, 2007). Also the positive trend in urban–rural inequality since

around 1980 (noted by many authors in the literature) vanishes once one allows for the fact that

the rate of inflation has been higher in urban areas than rural areas, although a marked positive

trend in urban–rural inequality since the mid-1990s is still evident.

In common with most countries, the bulk of the analysis of poverty and inequality in

China (and India) has relied on repeated cross-sectional surveys, in which the samples at each

date are treated as independent. Thus one does not track the living standards of the same

households over time. We do not then know how much of the poverty at one date is persistent,

and how much is transient (reflecting fluctuations in living standards, including movements in

and out of poverty). (Some lessons from panel data studies will be reviewed later in the paper.)

Lack of public access to the micro data for China has restricted the ability of researchers

to try to address these data concerns. However, the micro data have been available for some

selected provinces and time periods. Ravallion and Chen (1999) used the micro data for four

provinces of southern China to correct for both the valuation methods for consumption of own

product and the deflators. The corrections to the original survey data tend to entail lower

measured inequality and they attenuate the rate of increase in inequality over time.

Not all the likely data problems mean a lower true level of inequality or a lower rate of

increase over time. For example, if we could correct for selective compliance (whereby the

relatively well off are less well represented in surveys) then we may well find higher inequality.2

However, we currently have no basis for correcting this problem in either China or India.

Poverty monitoring in India since the 1960s has been mainly based on the household

expenditure surveys done as part of the National Sample Surveys (NSS). The salient features are

that household consumption expenditure per person is used as the individual welfare indicator

and the poverty line that is intended to have a fixed real value across time and space (urban and

rural areas of states) is determined by combined geographic and inter-temporal deflators. The

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main data issue is that assessing what has been happening to poverty and inequality in India

during the 1990s has been clouded by a comparability problem between the two main surveys

available for the 1990s.3 However, the surveys for 1993/94 and 2004/05 are highly comparable.

There are concerns about how well surveys measure incomes or consumptions. Survey-

based consumption and income aggregates for nationally representative samples typically do not

match the aggregates obtained from national accounts (NA). This is to be expected for GDP,

which includes non-household sources of domestic absorption. Possibly more surprising are the

discrepancies found with both the levels and growth rates of private consumption in the NA

aggregates; Ravallion (2003) provides evidence. The discrepancies between levels and growth

rates of consumption as measured by India’s NSS and NA have been of particular concern, Yet

here too it should be noted that (as measured in practice) private consumption in the NA includes

sizeable and rapidly growing components that are typically missing from surveys (Deaton,

2005).4 However, aside from differences in what is being measured, surveys do encounter

problems of under-reporting (particularly for incomes; the problem appears to be less serious for

consumptions) and the aforementioned problems of selective non-response.5

There are also a number of data problems in making comparisons between these

countries. These include that fact that China has traditionally used household income (per capita)

as the ranking variable while India has used consumption (per capita). (We return to this point

when we compare inequality measures.) Also, the available data on spatial differences in the

cost-of-living are still rather weak in both countries. And purchasing power comparisons

between the two countries are confounded by a number of concerns about the underlying price

data and standard index-number problems. We will largely ignore these data problems in this

paper, although that is not because we think them unimportant; rather, it is because this paper is

not the place to dwell on them.

2 This is not necessarily the case, but there is supportive evidence for the US (Korinek et al., 2006). 3 The comparability problem is discussed in Deaton (2001), Datt and Ravallion (2002) and Sen and Hiamnshu (2004a). 4 Deaton and Kozel (2005) provide a useful compilation of papers on this and related issues of poverty measurement in India. 5 In measuring poverty some researchers have replaced the survey mean by the mean from the national accounts (GDP or consumption per capita); see, for example, Bhalla (2002) and Sala-i-Martin (2002). This assumes that the discrepancy is distribution neutral, which is unlikely to be the case; for example, selective non-response to surveys can generate highly non-neutral errors (Korinek et al., 2005). For further discussion in the context of poverty measurement in India see Ravallion (2000).

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However, one data-related issue that should be flagged is how well conventional

inequality measures capture the significance attached to certain between-group inequalities.

Naturally, any conventional inequality measure puts weight on such differences. However, it is

far from obvious that those weights accord well with the significance attached to between-group

inequalities, as argued by Kanbur (2001). While this raises a number of deeper questions about

individualism and the role of group identities that are beyond our present scope, we will note the

extra significance attached to certain between-group disparities in both China and India.

3. Ways in which growth has been uneven

Growth in China and India over the last quarter century has indeed been uneven, which

has been apparent in several (related) dimensions, with implications for inequality, poverty

reduction and human development in the two countries. This section makes four claims:

• Growth was uneven across states in India and provinces in China and this has meant

uneven progress against poverty.

• Growth has been sectorally uneven, with primary sector growth rates lagging behind

growth rates in the secondary and tertiary sectors in both China and India, and with rural

incomes growing more slowly than urban incomes.

• There has also been uneven growth at the household level. In particular, incomes at the

top of the distribution increased much faster than those at the bottom in both countries.

That has meant rising inequality—dramatically so in the case of China.

• Because the more rapid growth of both countries has been so uneven in these dimensions,

it has sometimes brought disappointing outcomes in terms of progress against poverty

and other (“non-income”) dimensions of well-being.

Growth has been geographically uneven

The aggregate growth performances of China and India mask considerable unevenness of

growth at the sub-national level. Chinese provincial GDP growth rates (between 1978 and 2004)

ranged from a low of 5.9 percent in Qinghai to a high of 13.3 percent in Zhejiang. In India,

among the 16 major states, Bihar (including the newly created state of Jharkand) had the lowest

growth rate, namely 2.2 percent, while Karnataka had the highest, 7.2 percent.

While state and provincial-level growth rates in the last twenty five years have been

higher and less volatile than in prior decades—for instance, in India, except for the Green

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Revolution states of Punjab and Haryana and the state of Maharashtra, growth rates before the

1980s were at most 2 percent per annum—the variation in growth rates has meant increasing

regional disparities in both countries. The increase has been more pronounced in the case of

India where states that were initially poorer have grown more slowly, resulting in unconditional

divergence in both absolute and relative terms.6 This is apparent in Figure 2, which plots the

average annual growth rate of real per-capita state GDP against a state’s initial per-capita GDP

relative to the poorest state. India’s poorer states are still experiencing positive growth, but the

high growth rates, post reform, have been elsewhere.

In China, provinces that were initially poorer have managed to keep pace with the

initially wealthier provinces in terms of aggregate growth rates (Figure 2). That has meant no

divergence in relative terms, but absolute differences across provinces have increased. There

have also been signs of divergence regionally between the coastal and inland areas of China.7

The spatial unevenness of growth has contributed to uneven progress against poverty in

two ways. Firstly, because household-income growth has been closely associated with poverty

reduction at the sub-national level in both India and China,8 the fact that growth was

geographically uneven has meant that progress against poverty was uneven as well, with some

states and provinces seeing far more rapid reduction in poverty than others. In China, the coastal

areas fared better than inland areas. The trend rate of decline in the poverty rate between 1981

and 2001 was 8 percent per year for inland provinces, versus 17 percent for the coastal

provinces. In India, most of the western and southern states—peninsular India (with the

exception of Andhra Pradesh)—did comparatively well, while the more backward BIMARU

states of Bihar, Madhya Pradesh, Rajasthan and Uttar Pradesh, along with states in the eastern

region, achieved relatively little poverty reduction between 1993–94 and 1999–2000.

Secondly, in both countries, the most rapid growth did not occur where it would have had

the most impact on poverty. This is evident if one compares growth rates across provinces with

the growth elasticities of poverty reduction weighted by the initial shares of total poverty. (The

6 Econometric tests indicating more marked growth divergence for India in the post-reform period can be found in Ghosh (2006). 7 See Chen and Fleisher (1996), Jian et al. (1996), Sun and Dutta (1997), Raiser (1998) and Kanbur and Zhang (1999). Milanovic (2005) describes the regional inequalities within five federations, including China and India. 8 This is clearly documented for India by Datt and Ravallion (1996, 2002) and Deaton and Dreze (2002), and by Ravallion and Chen (2006) in the case of China.

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weights assure that this gives the impact on national poverty of growth in a given province.) Had

the pattern of growth favored provinces where growth would have had the greatest impact on

poverty, we would find a negative correlation between the growth rate and the share-weighted

elasticity. However, for neither country does one find any relationship, one way or the other (see

Ravallion and Chen, 2007, for China and Datt and Ravallion, 2002, for India.)

Growth has been sectorally uneven

Growth rates in the primary sector (agriculture) have not only lagged behind those in the

secondary (industry) and tertiary (services) sector, but have actually declined over the last

quarter century (Figure 3).

In nominal terms, urban incomes and expenditures have clearly increased faster than rural

incomes over the past quarter century in both countries. India has seen a steady increase in the

ratio of urban to rural mean real consumption levels from just below 1.4 in 1983 to about 1.7 in

2000. Even in 1981, the urban-to-rural ratio of nominal mean incomes in China was around

2.5—much higher than it has ever been in India. And since then, while there have been periods

when the ratio of urban-to-rural mean incomes fell, the overall trend has been upward.

Adjusting for cost-of-living differences clouds these trends somewhat. For China, the

urban rate of inflation has been higher than for rural areas and once one allows for this fact, one

no longer finds a trend increase over time in the ratio of the urban mean to the rural mean

(Ravallion and Chen, 2007).9 However, there have been sub-periods, including the period from

1997 to the present, during which the relative urban-rural disparity has risen. Moreover, even

allowing for cost-of-living differences, the absolute gap between rural and urban incomes has

increased appreciably. This is also true of India.

The sectoral composition of growth mattered for poverty reduction in both countries.

This can be seen clearly if we divide GDP per capita into three sources, primary, secondary and

tertiary, and estimate the following regression for the rate of change in the poverty measure:

ti

ititit YsP εππ +∆+=∆ ∑=

3

10 lnln (1)

9 There are other data problems with ambiguous implications for urban-rural disparities. The undercounting of rural migrants in China’s urban areas is likely to lead to an overestimation of the level and growth rate in the ratio of the urban mean to the rural mean. Against this effect, urban survey response rates tend to be lower than for rural areas and it be safely assumed that the rich tend to have lower response rates. Our discussions with the staff of China’s National Bureau of Statistics suggest that this problem is growing over time in China.

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where itY is GDP per capita from source i=1,2,3, titit YYs /= is the source’s share, and tε is a

white-noise error term. Note that the sector-specific growth rates are share-weighted in (1) to

allow for the fact that two sectors growing at the same rate will not have the same aggregate

impacts when one sector accounts for a smaller share of aggregate income than the other. When

share-weighted, one obtains a straightforward testable hypothesis for whether the composition of

growth matters; only in the special case in which ππ =i for i=1,2,3, does equation (1) collapse

to a simple regression of the rate of poverty reduction on the rate of GDP growth ( tYln∆ ).10

Table 1 provides regressions for the rate of change in poverty over time (that is, the

difference in the log of the headcount rate of poverty) on both the overall rate of per-capita GDP

growth (that is, the change in the log of GDP per-capita), as well as the share-weighted rates of

growth of GDP in each of the three sectors (equation 1).

For China, the overall elasticity of the headcount index to GDP growth was –2.6.

However, when one decomposes growth by sector, it is clear that its composition mattered

greatly to the rate of poverty reduction. The impact of growth in the primary sector was far

higher (by a factor of about four) than for growth in either the secondary or tertiary sectors. The

impacts of the latter two sectors are similar.

For India, too, the sectoral composition of growth was important, although tertiary sector

growth was relatively more important than in China.11 This probably reflects the difference

between the two countries in the distribution of agricultural land. In rural China, starting

conditions at the outset of the reform process entailed relatively low levels of inequality in access

to land. The de-collectivization process that started in the late 1970s achieved a relatively equal

allocation of access to agricultural land, at least within communes. (Between communes, the

only way to equalize land allocation would have been to allow mobility of people, which was not

considered a desirable option.) This meant that agricultural growth was a powerful instrument

against poverty and inequality in China (Ravallion and Chen, 2007). The distribution of

agricultural land was and is clearly more unequal in India, and that naturally attenuates the

10 This test is due to Ravallion and Datt (1996). Note that these regressions are best viewed as decomposition tools rather than causal models of poverty reduction. Deeper explanations must endogenize growth rates and their composition; Ravallion and Chen (2007) provide models of poverty reduction in China that try to make some progress in that direction. 11 Note that the coefficients on secondary and tertiary-sector growth for India are of approximately equal size but opposite sign (table 1). This suggests that the (share-weighted) difference in growth rates is picking up a distributional effect on poverty reduction.

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impact of agricultural growth on poverty relative to that found in China. Note also that India’s

overall growth elasticity of poverty reduction is appreciably lower than China’s (Table 1).

Increases in rural incomes, whether from agricultural growth or (particularly in the case

of China) from increased rural non-farm employment, also turn out to have been critical for

overall poverty reduction. Table 2 gives regressions of the rate of change in poverty over time

(difference in the log headcount index) on the share-weighted growth rates of rural and urban

mean incomes and a term capturing the effect of any shifts in population from rural to urban

areas. It can be seen that in both countries, growth in rural incomes is the only statistically

significant correlate of poverty reduction. Ravallion and Chen (2007) also report an alternative

decomposition for China, which exploits the analytic (additivity) properties of the headcount

index, whereby the national index is the population-weighted mean of the urban and rural

indices. This decomposition makes somewhat different assumptions to the regression

decomposition. However, it confirms the quantitative importance of rural economic growth;

about 72 percent of the reduction in the headcount index that occurred in China between 1981

and 2001 is attributable to rural poverty reduction, versus 5 percent due to urban and 23 percent

due to the population shift from rural to urban areas.

The results in Tables 1 and 2 imply that the particular form of sectorally uneven growth

China and India experienced—primary sector growth rates lagging behind growth rates in the

secondary and tertiary sectors, and rural incomes growing more slowly than urban incomes—has

meant less poverty reduction than might have been the case otherwise. A sense of how much

extra poverty reduction might have been achieved from a more balanced growth path can be

obtained through counterfactual simulations in which it is assumed that all three sectors grow

equally—meaning that the sector shares of GDP in 1981 would have remained constant over

time—and the estimates from Table 1 are used to calculate the implied rate of poverty reduction

under different assumptions about the overall (common) rate of GDP growth. So, for instance,

had it been possible to achieve a balanced growth path while maintaining the GDP growth rates

China actually achieved between 1981 and 2001, the mean rate of poverty reduction would then

have been 16.3 percent per year, rather than 9.5 percent. Instead of 20 years to bring the

headcount index down from 53 percent to 8 percent it would have taken about 10 years.

A similar exercise for India suggests that were it not for the sectoral and geographic

imbalance of growth, the national rate of growth since reforms began in full force in the early

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1990s would have generated a rate of poverty reduction that was double India’s historical trend

rate (Datt and Ravallion, 2002). The evidence also suggests that states with relatively low levels

of initial rural development and human capital development experienced lower elasticities of

poverty reduction to economic growth (Ravallion and Datt, 2002).

Of course one can question whether in fact a more sectorally balanced growth path could

have been achieved without lowering the overall growth rate, and so this exercise should be

viewed as an upper bound on what might have been possible. There do appear to be signs of a

sectoral tradeoff in that the correlation between China’s primary sector growth rates and the

combined growth rate of the secondary and tertiary sector was –0.41 over this period, implying

that a more balanced growth path in which the growth rate of the primary sector was higher

might have meant less growth overall. But it is worth noting that the negative correlation is

statistically quite weak—a significance level of 6 percent—and that there were sub-periods

(1983–84, 1987–88 and 1994–96) in which both primary sector growth and combined growth in

the secondary and tertiary sectors were both above average (Ravallion and Chen, 2007). Later

we return to the question of whether either country faces an aggregate growth-equity trade-off.

Income growth has been uneven across households

The unevenness of economic growth across households at different levels of living can be

seen clearly in the growth incidence curve (GIC), which gives the annualized rate of growth over

the relevant time period at each percentile of the distribution (ranked by income or consumption

per person).12 Figure 4 gives the growth incidence curves for China and India, for 1993 to

2004/5. In both cases, growth rates at the bottom of the distribution were lower than those at the

top. The gradient is less steep for India. Growth rates in China for the richest percentiles were

about double those for the poorest. Strikingly, however, even the growth rates for the poorest

percentile in China exceed those for the richest percentile in India over this period.

Figure 4 may well understate growth rates for the richest. As we have noted, large

sample nationally representative surveys (such as used to estimate Figure 4) do not typically pick

up what is happening at the extreme upper tails of the distribution. In the case of India, evidence

from other sources indicates that incomes at the top end have risen dramatically. For instance,

Banerjee and Piketty (2005), based on a study of tax returns, report that the super-rich in India—

12 On the precise definition and properties of the GIC see Ravallion and Chen (2003).

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that is, those at the 99.99th percentile—experienced growth in incomes of over 285 percent

between 1987/88 and 1999/00, resulting in annual PPP incomes of around $160,000 per person.

Figure 5 displays the trends in income inequality for the two countries. From a cross-

country perspective, India remains a relatively low-income inequality country (World Bank,

2005, 2007), although this is no longer true of China. The Gini index of income inequality for

China rose from 28 percent in 1981 to 41 percent in 2003, though not continuously, and more in

some periods and provinces.13

Note that the fact that the inequality measures for China use income while those for India

use consumption (per capita) does not account for the difference in measured inequality as in

Figure 5. For a few years it is possible to measure inequality using consumption for China. When

one does, the consumption-based inequality measure is only slightly lower than that based on

incomes, and it is still appreciably higher than for India (Chen and Ravallion, 2007).

In the case of India, one finds that the Gini index rose in the 1990s, although the increase

was far less pronounced than in China (Figure 5).14 It is too early to say if India is undergoing a

trend increase in inequality similar to what China has experienced. As can be seen from Figure 5,

on looking back over time, rising inequality in India is seen to be a recent phenomenon.15 Indeed,

there is no statistically significant trend increase in consumption inequality in India up to the

early 1990s (Bruno et al., 1998).16

Perceptions “on the ground” that inequality is rising markedly in India do not appear to

sit easily with the impression given by Figure 5. Popular opinion can be mistaken, but nor are the

data perfect. As we have noted, the survey-based numbers may well understate the relative gains

to the rich, and that is consistent with the evidence from tax returns. The visible changes in

13 Note that the latter figure is somewhat lower than past estimates for China; this is because corrections have been made for changes in survey-valuation methods (as discussed above) and urban-rural cost-of-living differences, which have tended to rise over time because of higher inflation in urban areas (as price controls and subsidies were progressively removed on certain goods, including housing). Without these corrections, the estimate of the Gini index for 2003 rises to over 45 percent instead of 41 percent. 14 Figure 5 uses the NSS “thick samples” only. The thin samples for the 1990s also confirm the increase in inequality (Ravallion, 2000). 15 Note that longer term comparisons are only possible using the uniform recall period data, using the Deaton method of correcting for the comparability problem in the 1999/00 data. 16 At the time of writing, the 61st round of the NSS, for 2004/5, has not yet been released. This will give a (keenly awaited) indication of whether the signs of rising inequality in the 1990s have been sustained. Only preliminary tabulated results were available to us at the time of writing, but they suggest that the increase in inequality in India since the early 1990s that is evident in figure 6.5 has continued through to 2005, and may well have accelerated, although this should be investigated more thoroughly when the micro data become available.

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consumption patterns and lifestyles that the rich have achieved may well not be reflected

properly in the survey-based inequality measures. Also, and possibly more importantly, the

perception of sharply rising inequality in India may well also reflect rising absolute inequality, as

reflected in the absolute gaps between the rich and the poor, as distinct form the proportionate

gaps. There is evidence that many people view inequality in absolute terms rather than relative

terms (Amiel and Cowell, 1999).17

The unevenness of growth has contributed to rising inequality

Since both countries started their reform periods with sizeable rural–urban gaps in mean

living standards, the unevenness of the subsequent growth process, in which urban incomes

increased faster than rural incomes, is likely to have put upward pressure on aggregate

inequality. The time series data and regressions presented in Ravallion and Chen (2007) provide

direct evidence of this for China. Controlling for growth in rural and urban incomes, the rising

urban population share has had no significant effect on aggregate inequality, and the periods

when the urban–rural disparity in mean income rose (fell) were the periods when overall

inequality rose (fell). But it would also seem that the rising urban–rural gap now has a salience in

popular and governmental circles that far exceeds its likely contribution to a conventional

inequality or poverty metric. This appears to stem in part from the (plausible) belief that the

urban–rural divergence reflects (in part at least) urban biases in the reform processes and

complementary public spending choices. This is reinforced by actual or perceived abuses of local

political powers at the expense of poor farmers or the landless rural poor (the recurrent land

disputes of land contracts and land-use conversions in rural China are examples).

Similarly, regional inequality concerns loom large in both countries, although the

quantitative importance of increasing disparities across regions (provinces and states) appears to

be greater in India. While these between-group inequalities have carried weight in policy

discussions, it is important to note that growing inequality within both urban and rural areas have

been a major component of the increase in overall inequality; for China rising inequality within

rural areas has been an important dynamic in overall inequality while in India inequality has

risen more within urban areas than rural areas.

17 For further discussion of the implications of the absolute-inequality concept for assessments of economic growth and reform see Ravallion (2004a) and Atkinson and Brandolini (2004).

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The sectoral composition of GDP growth—cutting across the urban and rural divide —is

also a significant correlate of the changes in inequality. For instance, regressions of the sort

reported in Table 1, with share-weighted sectoral GDP growth rates as covariates, but with the

change in inequality (change in the log of the Gini index) as the dependent variable indicate that

in China, primary sector growth has been associated with lower inequality overall, while there is

no correlation with growth in either the secondary or tertiary sectors (Ravallion and Chen, 2007).

There is also evidence of a lagged primary-sector growth effect, with a similarly sized impact as

the current year’s effect. Regressing the change in log national Gini index ( tGln∆ ) on the two-

year moving average of the primary sector growth rate ( tY1ln∆ ) one obtains:

Gtttt YYG ε̂2/)lnln(746.00522.0ln 111

)723.3()563.4(+∆+∆−=∆ −−

R2=0.463; n=20 (2)

Note that the intercept indicates a strong positive trend in inequality, of about 5% per annum.

Of course, other factors impinged on the changes in aggregate inequality in China besides

changes in agricultural incomes. Two other factors that appear to have influenced aggregate

inequality, side-by-side with agricultural growth rates, are the pace of urbanization and the rate

of inflation. Adding these to the above regression we obtain:18

Gtttttt PUYYG ε̂ln175.0ln969.02/)lnln(744.0083.0ln

)247.2(1

)670.2(111

)346.3()104.4(+∆+∆−∆+∆−=∆ −−−−

R2=0.605 (3)

Here U denotes the urban population share and P is the Consumer Price Index (we used the rural

index). The impact of primary sector growth remains strong, but we also find an inequality-

decreasing effect of urbanization,19 and that higher inflation rates entail higher inequality. The

pro-poor distributional effect of lower inflation is consistent with evidence for other countries.20

How much higher would the rate of primary sector growth need to have been to stem the

rise in aggregate inequality? The above elasticity implies that a (moving average) growth rate of

7.0 percent per annum would be needed to avoid rising inequality, whereas the mean primary-

sector growth rate was 4.6 percent per annum between 1981 and 2001. Only in two periods, the

18 There is also evidence of a significant effect of past inequality; adding 1ln −∆ tG its coefficient is

-0.348 (t-ratio=-3.11); other coefficients remains significant and the R2 rises to 0.681. However, with 20 observations (one of which is lost in the dynamic specification) it is not clear how robustly one can expect to identify dynamic effects. We confine this discussion to the simpler model in the following regression. 19 The fit improved with a one year lag in the effect of urbanization, which is plausible. 20 Including Easterly and Fischer (2001) and Dollar and Kraay (2002) both using cross-country data, and Datt and Ravallion (1998) using data for India.

15

early 1980s and the mid-1990s, were agricultural growth rates in China high enough to prevent

rising inequality. The divergence between the actual growth rates in the primary sector GDP and

the minimum needed to prevent rising inequality is particularly striking in the most recent

period. The recent composition of economic growth in China has clearly been inequality

increasing.

It is too early to say with confidence that India’s (more recent) rise in inequality stems

from similar factors. Nonetheless, we can be reasonably sure that the “urban bias” in India’s

growth process since reforms began has put upward pressure on overall inequality.

4. Why growth was uneven and why this matters

Why was growth uneven—in the aggregate as well as sectorally and geographically—and

what is one to make of this unevenness? Should the fact that in both India and China, segments

of the population appear to have been left behind (at least thus far) be of concern? And should

we worry that inequality has risen?

These questions are more easily posed than answered because of the multiple complex

processes through which uneven growth and inequality are generated and reproduced. Policies

play a role but so do initial conditions in the form of history (for example, inherited institutions)

and geography (as a determinant of access to markets and public services). Economic forces are

undoubtedly important, but so too are political and social factors. Answering these questions in a

rigorous fashion is beyond the scope of this paper. What we can do, however, is provide an

assessment based on our interpretation of the evidence from various sources.

We structure the discussion around a distinction between good and bad inequalities—

drivers and dimensions of uneven growth that are good or bad in terms of what they imply for

how the living standards of poor people evolve over time. We argue that the post-reform

development paths of both India and China have been influenced by and have generated both

types of inequalities.

Good inequalities

Good inequalities are those that reflect and reinforce market-based incentives that are

needed to foster innovation, entrepreneurship and growth. Scattered evidence suggests that the

rise in inequality with the introduction of market reforms in both India and China is at least in

part a reflection of newly-unleashed market-based incentives at work, in contrast with the earlier

16

period of artificially low levels of inequality brought about by regulatory distortions and

interventions that suppressed incentives for individual effort and innovation.

Perhaps the leading example of the role that good inequalities (and the economic

incentives that underlie them) have played in China’s growth is the stimulus to agricultural

production in the early 1980s provided by the Household Responsibility System (HRS). Under

the HRS, rural households were assigned plots of land and became the residual claimants on the

output from that land, significantly enhancing the incentives for production. Prior to that, land

had been farmed collectively, with all members sharing the output more-or-less equally.

Incentives for individual effort in this setting were naturally very weak, and the reforms to this

system were critical in stimulating rural economic growth at the early stages of China’s transition

(Fan, 1991; Lin, 1992). Initially these reforms are likely to been inequality-reducing, by raising

rural incomes relative to urban areas. However, soon some farm-households did better than

others—depending on farming acumen, agro-climatic conditions and access to markets—putting

upward pressure on inequality within rural areas.

Another example is provided by Park et al. (2004) in their analysis of the rise in urban

wage dispersion in China during the current reform period. At the outset of that period, urban

China had a system of fixed wage scales, allocation of labor by government and (hence) low

returns to schooling (Fleisher and Wang, 2004). There were few incentives for work-effort or

skill-acquisition. From this legacy of wage compression and low labor mobility, China moved

gradually in the 1990s to a market-based system featuring a dynamic non-state sector and an

increasingly open labor market. With reforms that expanded the scope for employment in a

growing private sector and the emergence of a competitive labor market, wage dispersion within

skill categories and experience cohorts has increased considerably and returns to schooling have

risen (Park et al., 2004; Heckman and Li, 2004). Looking forward, a further implication of the

emergence of a more convex structure of returns to education in post-reform China (whereby the

increase in returns to education has tended to be at higher levels of schooling) is that generalized

increases in the level of schooling will put upward pressure on aggregate inequality, though they

will probably be poverty reducing. In India, too, one sees growing variance in wages, attributable

in part to increasing wage dispersion within educational attainment categories, which in turn

reflect increasingly competitive product and labor markets (Dutta, 2005).

17

Yet another example of how increasing disparities might reflect the growth-enhancing

role of incentives comes from considering the increasing variation in the growth performance of

Indian states during the 1990s, when some states significantly accelerated their growth, while

others lagged behind. Both Ahluwalia (2000) and Kohli (2006) conjecture that the increase can

be attributed, at least in part, to the greater responsiveness of private investment flows to

differences in the investment climate in different states. As Kohli (2006) notes, that in turn

appears to have provided—subject to the constraints imposed by state-level political

considerations and capacity—some incentives to state leaders to adopt measures to improve the

business environment, to woo private investment. This stands in contrast to earlier periods when

the share of public investment in total investment was much larger.

There is evidence that the impact of incentives was magnified by the presence of

agglomeration economies in industrial activity in India. Lall and Chakravorty (2005) show that

industrial diversity (which is higher in metropolitan and mixed industrial regions) has cost-

reducing effects, through agglomeration economies. Because of this, private industrial units

favored locating in existing high-density industrial areas, increasing the degree of industrial

clustering. On the other hand, the location decisions of state-owned industry appeared to have

been much less driven by these cost considerations and were possibly motivated by a desire for

greater regional balance. The conclusion that Lall and Chakravorty draw is that the reforms and

the scaling back of public investments, and the emergence of the private sector as the primary

source of new industrial investments that was associated with it, contributed to higher levels of

spatial inequality in industrial activity.

Bad inequalities

The processes underway in India and China are almost certainly less benign and less

automatic than the account above suggests. Geographic poverty traps, patterns of social

exclusion, unequal opportunities for enhancing human capital, lack of access to credit and

insurance, corruption and uneven influence can all conspire to simultaneously fuel rising

inequality and prevent certain segments of the population from making the transition out of

traditional low-productivity activities. Credit market failures often lie at the root of the problem;

it is poor people who tend to be most constrained in financing lumpy investments in human and

physical capital. These bad inequalities—rooted in market failures, coordination failures and

18

governance failures—prevent individuals from connecting to markets and limit investment in

human and physical capital.21

We focus on two dimensions of bad inequalities. The first relates to location in the

presence of externalities, impediments to mobility and heavy dependence of local states on local

resources. These features can generate geographic poverty traps whereby living in a well-

endowed area entails that a poor household can eventually escape poverty, while an otherwise

identical household living in a poor area sees stagnation or decline. This is one possible reason

why initially poorer provinces have often seen lower subsequent growth (Figure 6).

While these observations from aggregate data are suggestive that such traps might exist,

they are hardly conclusive. More rigorous micro evidence of the geographic externalities that

underlie such traps can be found in Jalan and Ravallion (2002) and Ravallion (2005), using farm-

household panel data for rural China. The geographic attributes conducive to individual

prospects of escaping poverty include both publicly-controlled endowments (such as the density

of rural roads) and largely private ones (such as the extent of agricultural development locally).

The second dimension of undoubted importance relates to inequalities in human resource

development—often linked to credit market failures on the demand side but also reflecting

governmental failures in service delivery. We argued above that the rising returns to schooling

and increasing dispersion of wages represent ‘good’ inequalities because they reflect freer labor

markets with increased incentives for work and skill-acquisition. But naturally, those with

relatively little schooling and few assets, or little access to credit are less able to respond these

incentives and are less well positioned to take advantage of the new opportunities unleashed by

market-oriented reforms. And thus, inequalities in human capital are ‘bad ineqaulities’ in that

they have retarded poverty reduction through growth in both countries.22

Basic schooling was far more widespread in China at the outset of the reform period than

in India; China has achieved close to universal primary education. But inequalities in educational

attainment beyond primary school remain, and have become an increasingly important source of

21 World Bank (2005, Chapter 5) provides a useful overview of the arguments and evidence on how certain inequalities can be inefficient, notably when they entail unequal opportunities for advancement. Also see the excellent overview of the theoretical arguments in Aghion et al. (1999). 22 Note that the claim that inequalities in human capital are “bad” is not inconsistent with our earlier point that certain inequalities in outcomes, reflected for instance in increasing wage dispersion, are “good.” The latter stem from variation in returns that reflect differences in effort. The former arises from differences in endowments that are the result of both supply-side governmental failures and demand-side market failures (especially in credit-markets).

19

disadvantage because a junior high school education, and in some instances, a senior high school

education, has become a de facto prerequisite for accessing non-farm work particularly in urban

areas, where wages far exceed the shadow wages in farming. Thus, lack of schooling is now an

important constraint on prospects of escaping poverty in China, as elsewhere.

India’s schooling inequalities are clearly larger than those of China (both at the beginning

of the reform period and since).23 Inequality of schooling attainments has clearly been an

important factor inhibiting pro-poor growth. The differences we have seen in the impacts of non-

farm economic growth on poverty reflect inequalities in a number of dimensions; low farm

productivity, low rural living standards relative to urban areas and poor basic education all

inhibited the prospects of the poor participating in growth of the non-farm sector (Ravallion and

Datt, 2002). Interstate differences in initial levels of schooling appear to have been the dominant

factor in explaining the subsequent impacts of non-farm economic growth on poverty. Those

with relatively little schooling and few assets, or little access to credit, were less well positioned

to take advantage of the new opportunities unleashed by market-oriented reforms.

Policy impediments, policy biases and policy neglect

Errors of both omission and commission in policy have contributed to the unevenness of

growth in both countries, and the failure of growth to translate into larger impacts on poverty and

human development. These errors have taken one of three forms: first, policies that have

impeded the functioning of markets; second, policies that have been biased in favor of particular

regions or industries; and third, policies that have neglected certain spheres of activity where

public interventions were in fact necessary.

In India, it has been argued that restrictive labor regulations and widespread preferences

in favor of small-scale industries are impediments to more broad-based growth. While motivated

(ostensibly) by distributive considerations, these policies are believed to have instead restricted

firm growth, dampened job creation and hindered the movement of labor out of agriculture in

India (World Bank, 2006). Only eight million workers are protected by this legislation, in a

country of one billion; “Current labor regulations seem to be protecting workers in jobs by

‘protecting’ other workers from having jobs.” (World Bank, 2006, p.17). These regulations are

unlikely to have helped labor absorption, and may well have helped create a situation in which

the fraction of the labor force in India that remains in agriculture far exceeds that of other

20

countries with similar shares of agricultural value added (Virmani, 2005). And despite the

increase in GDP growth, the rate of job creation in India has failed in recent years to keep up

with increases in the size of the labor force, which has led some to characterize India’s growth

experience as “jobless growth” (Mehta, 2003). While these observations are suggestive, the costs

to the poor of these policies have yet to be rigorously quantified.

In China, impediments to the movement of labor out of agriculture through internal

migration have come in part from governmental restrictions under the houkou system, whereby a

person has to have an official registration to reside in an urban area and use its facilities. Those

born with an agricultural registration have had a hard time getting urban registration.24 Other

costs of migration facing rural households include the risk of losing one’s (administratively

allocated) land allocation at the origin and various forms of discrimination against rural migrants

in urban areas. A rough estimate of the magnitude of these policy-induced costs of migration is

provided by Shi et al., (2004) who report that even after controlling for worker characteristics

and cost-of-living differences provide, urban wages are about 50 percent higher. The high costs

of migration underlying these gaps have probably been both poverty and inequality increasing.

There are also similar restrictions on within-rural and within-urban migration.

Sizeable aggregate output losses from these inequality-increasing restrictions on

migration are likely. Not only is labor miss-allocated across sectors, but the restrictions make it

harder for China to realize agglomeration economies (Au and Henderson, 2006). Under the

(plausible) assumption that these costs of migration lower earnings in the poorer (labor-surplus)

sector, they will increase poverty and inequality. Other policy biases against the poor have

included public spending and industrial policies that have favored China’s coastal areas over the

(poorer) inland regions.

In both countries, an important area of policy neglect has been service delivery. The

deficiencies of India’s education system (and not just from the point of view of the poor) are well

known (Drèze and Sen, 1995; PROBE, 1999). Issues of service quality loom large in these

concerns (World Bank, 2006). While starting from greater equity in service delivery (though

even then with large gaps between urban and rural areas) China has also seen growing

23 Evidence supporting this claim can be found in World Bank (2005). 24 In spite of this, China has still had a more rapid rate of urbanization than has India. China’s urban population share rose from 19 percent in 1980 to 39 percent in 2002. In India (with no such restrictions) the urban share of the population rose from 23 percent to 28 percent over the same period.

21

inequalities in access to health and education (Zhang and Kanbur, 2005). The weaknesses and

inter-regional disparities in service delivery in both countries can be traced, at least in part, to the

large and rising disparities in public spending per capita between rich and poor areas, with rather

weak fiscal redistribution and (hence) heavy dependence of local governments on local

resources. We return to this point when we discuss policies.

Dynamics: Good inequalities can turn into bad ones

Without the appropriate institutional checks and balances, rising inequality, even if it is

initially of the “good” variety, can engender phenomena such as corruption, crony capitalism,

rent-seeking or efforts by those who benefit initially from the new opportunities to restrict access

of others to those opportunities or alter the rules of the game so as to preserve their initial

advantages.25 Thus bad inequalities emerge over time.

The growth and subsequent performance of China’s township and village enterprises

(TVEs) provides an example of this dynamic at work. The emergence and growth of TVEs in

various parts of China starting in the mid-1980s is often cited as a successful example of the

country’s strategy of incremental institutional innovation—in this case, economic

decentralization under which local governments were given the right to establish TVEs and

retain the profits generated by them (Oi, 1999). The implied autonomy and control, combined

with a hard budget constraint imposed from above provided exactly the right incentives at the

outset to invest and operate efficiently. The resultant increase in rural non-farm output and

employment, was spatially uneven, but probably inequality reducing overall (given the rural base

for this innovation) and contributed to China’s growth up to the mid-1990s.

However, with the proliferation of TVEs and the increased competition this implied in

various product categories, pressures emerged for local and provincial governments to protect

the (local) markets of the TVEs and enterprises under their control. The result was increasing

impediments to inter-jurisdictional trade and to entry by outside firms, leading to fragmentation

of China’s domestic product and factor markets and a deterioration in the investment climate in

many localities (World Bank, 2005).

Perceptions and tolerance for inequality: The “bad” can drive out the “good”

Bad inequalities are doubly harmful. First, they directly reduce the potential for growth

because segments of the population are left behind, lacking the opportunity to contribute to the

25 Antecedents to this argument can be found in the writings of North (1990) and Hellman (1998).

22

growth process. Second, on top of these direct human and economic costs, persistent bad

inequalities in a setting of heightened aspirations can yield negative perceptions about the

benefits of reform. Because it is difficult for citizens to disentangle the sources of the aggregate

inequality in observed outcomes—to determine whether the underlying drivers are good or

bad—societal intolerance for inequality of any kind emerges. And that can trigger social unrest

or harden resistance to further needed reforms, thereby (indirectly) threatening the sustainability

of growth. In effect, the persistence of bad inequalities drives out the good ones.

In China, Han and Whyte (2006) report results of a survey of over 3,000 Chinese adults

interviewed in 2004; 40 percent of respondents “strongly agreed” that inequality in the country

as a whole is “too large” while a further 32 percent “agreed somewhat” with this view. An

astonishing 80 percent favored “governmental leveling” to assure a “minimum standard of

living” (split roughly equally between “strong agree” and “agree somewhat”). Interestingly, the

correlates of perceptions of unfair inequalities did not suggest that the concern was greatest

among those most disadvantaged, such as farmers or migrants from rural areas. Also notable is

that most respondents did feel that education, ability and effort were rewarded in China.

Such high levels of concern about inequality do not imply dissatisfaction with the

distributive outcomes of economic reforms. However, there are also signs that perceptions of (or

direct experience with) bad inequalities is translating into growing dissatisfaction with reforms in

both countries. Social protests about various perceived injustices are becoming common in

China. Land disputes have been especially common in rural and peri-urban areas, where the

poorly-defined land rights of farmers leave ample opportunities for local officials and developers

to expropriate a large share of the rent from land-use conversions. In a poll conducted by the

Chinese Academy of Social Sciences in 2002, 60 percent of the 15,000 respondents thought that

party and government officials had benefited the most from reforms, while other polls (cited in

Pei, 2006) consistently rank corruption as one of the most serious problems facing China. In

examining the economic underpinnings of social unrest in China, Keidel (2005) makes the point

that dissatisfaction with the economic dislocations caused by reforms, which are a necessary part

of engendering good inequalities, have been amplified by corruption and malfeasance within

state-owned enterprises and local governments. Police records reported in official bulletins cited

by Gill (2006) indicate that the number of collective protests, violent confrontations and

demonstrations deemed to be incidents of social unrest has risen nearly tenfold from 8,300 in

23

1993 to almost 80,000 in 2005. Pei (2006) argues that such social discontent has made it more

difficult for China to undertake the reforms needed to address remaining structural weaknesses,

notably in the financial system—reforms that a study by the IMF (2003) suggest might be critical

to sustaining growth. Thus, Pei (2006) talks of China’s “trapped transition.”

In India, the political failure of the “Shining India” electoral campaign of the BJP in 2004

has been widely attributed to its neglect of the emerging inequalities in the wake of pro-growth

reforms. Such attributions are always questionable, but there is also evidence from attitudinal

surveys suggesting that rising inequality is a popular concern in India. In a 2004 National

Election Survey, three quarters of the respondents indicated that the reforms of the past decade

and a half had only benefited the rich (Suri, 2004). Within India’s democratic polity, such

sentiments have resulted in political pressures that have forced the government to postpone

needed reforms (Bardhan, 2005). For instance, last year the government had to withdraw plans to

privatize 13 leading industrial public-sector undertakings. The concerns about rising inequality

and slow progress against poverty have also led to various new antipoverty programs, which we

return to.

5. Preserving the good inequalities and reducing the bad ones

Putting in place the right mix of policies and institutions to ensure that growth is

sustained and broad-based is now high on the policy agenda of both governments. While

inequality has been prominent in (at least) the rhetoric of Indian politics for decades, it is a

relatively new concern in China, although it has emerged as a major concern in recent years.26

Should policy makers be so worried about rising inequality? Possibly it is inevitable to

some degree. Over five decades ago, Arthur Lewis (1954) observed that the defining feature of

structural transformation in economies with large pools of surplus labor is the gradual transfer of

surplus labor from “traditional” low-productivity activities to “modern” high-productivity

activities. Lewis argued that this process is inevitably accompanied initially by rising levels of

inequality as some make the transition and others are, at least temporarily, left behind.27 As

26 Han and Whyte (2006) quote results of a survey in 2004 of senior public officials done by the Communist Party’s Central Party School which found that income inequality was the highest expressed concern, dominating all other issues.. 27 The dimensions along which this productivity divide are manifested—rural vs. urban, traditional vs. modern agriculture, agriculture vs. industry, etc.—will naturally vary from context to context, even within a country.

24

Lewis put it: “Development must be inegalitarian because it does not start in every part of the

economy at the same time.”

If indeed what we are witnessing in China and India is such a process of structural

transformation, it may only be a matter of time before those left behind catch up. The rise in

inequality would then be a transitional phenomenon, although because the transition is occurring

on a decadal scale (even for a rapidly changing society and economy such as China’s), inequality

might continue to rise for several more years. And even when the transition is complete, because

of the good drivers of inequality set in motion by the reforms, there will almost certainly be an

increase in the steady-state inequality relative to that in the pre-reform period.

However, as this paper has argued, there are also a number of reasons to suggest that

policy makers concerned with assuring rising absolute levels of living, especially for the poor,

should be concerned about the “bad inequalities.” In the this section we try to provide a simple

conceptual framework for thinking about what policy makers in China and India should do about

rising inequality, and review some of the policy options, including those recently implemented in

both countries.

Defining the challenge and avoiding misdiagnoses

We take it to be self-evident that that the objective is sustainable pro-poor growth, by

which we mean growth that benefits poor people, so as to bring large and lasting reductions in

the extent of absolute poverty. Efforts to attenuate the bad inequalities should not then

undermine the drivers of good inequality to the point where the longer-term living standards of

poor are threatened. The challenge will be to identify the mix of policies that directly target the

bad inequalities without undermining the good ones.

From that starting point, it is clear that we should not accept redistributive policies that

come at the expense of lower longer-term living standards for poor people. Accepting that there

is no aggregate trade-off between mean income and inequality does not mean that there are no

trade-offs at the level of specific policies. Reducing inequality by adding further distortions to an

economy may well have ambiguous effects on growth and poverty reduction. But nor should it

be presumed that there will be such a trade-off with all redistributive policies. The potential for

“win-win” policies stems from the fact that some of the factors that impede growth also entail

And which dimensions are most relevant will clearly matter for thinking about policy. The larger point, however, is that there is some axis along which the dualism is manifested.

25

that the poor share less in the opportunities unleashed by growth. More rapid poverty reduction

requires a combination of more growth, a more pro-poor pattern of growth and success in

reducing the antecedent inequalities that limit the prospects for poor people to share in the

opportunities unleashed by a growth economy.

Learning from the past: Avoiding false tradeoffs

The experience of China and India over the last quarter century offers important lessons

regarding the broad policy directions that are necessary and possible. The first is that the idea of

an aggregate tradeoff between growth and equity is often, though not always, a false one. As we

have argued above, the trade off exists for certain inequalities but not others. The right

combination of policies can yield win-win-win combinations of growth, reduction in poverty and

declining (or at least non-increasing) inequality.

Testing for the existence of an aggregate growth-equity trade off poses a number of

analytic problems. In the case of China, it is at least suggestive that on comparing growth rates

with changes in inequality over time one finds no sign that higher inequality has been the price

of China’s high growth. The correlation between the growth rate of GDP and log difference in

the Gini index is only –0.05; the regression coefficient has a t-ratio of only 0.22. This test does

not suggest that higher growth per se meant a steeper rise in inequality. While the level of

inequality rose at the same time that average income rose, this reflects their common time trends

rather than genuine co-movement. The periods of more rapid growth did not bring more rapid

increases in inequality; indeed, the periods of falling inequality (1981–85 and 1995–98) had the

highest growth in average household income. Also, the sub-periods of highest growth in the

primary sector (1983–84, 1987–88 and 1994–96) did not come with lower growth in other

sectors (Ravallion and Chen, 2006). Nor does one find that the provinces with more rapid rural

income growth experienced a steeper increase in inequality; if anything it was the opposite.

The sources of higher primary sector growth rates in China were probably very different

between the early 1980s and the mid 1990s. In the former period, agricultural growth was

stimulated (in large part we expect) by the much-enhanced incentives for production achieved by

the introduction of the household-responsibility system (as discussed above), whereby farmers

became the residual claimants on farm output.28 In the second period (the mid-1990s) the higher

28 The literature has pointed to the importance of the reform to this system in stimulating rural economic growth at the early stages of China’s transition (Fan, 1991; Lin, 1992).

26

agricultural incomes appear to have come from a substantial reduction in implicit taxation of the

sector. From the early 1980s to the mid-1990s, the government has operated a domestic food

grain procurement policy by which farmers are obliged to sell fixed quotas to the government at

prices that are typically below the local market price (but were left free to sell the remainder at

market prices). For some farmers this was an infra-marginal tax, given that they produce more

food grains than their assigned quota, but for others it will affect production decisions at the

margin; Ravallion and Chen (2006) provide evidence that the reduction in this implicit tax

brought substantial income gains to the rural economy and especially to the poor.

Helping the rural poor connect to markets

Attenuating the rise in inequality and assuring more rapid poverty reduction will require

raising incomes in the lagging rural areas of both countries and this will require improved access

to markets. The question of how this should be done is often framed as a choice between

investing in poor-area development (jobs to people) or facilitating out-migration (people to jobs).

Posing the choice this way almost certainly over-simplifies the problem. Migration to urban

areas is likely to be pro-poor in both countries. However, out-migration will often not be feasible

for poor rural households without the right sort of investments in poor areas, both in human

resource development and agriculture. Conversely, while there may be scope for further

increases in agricultural incomes (e.g., through diversification into higher value crops) or for

promoting rural non-farm employment, the share of agriculture in GDP is bound to decline in

both countries, and geography and remoteness limit the possibilities of non-agricultural

economic activity in poorer regions.

We instead frame the question in terms of identifying and correcting the underlying

market and governmental failures and redressing the asset inequalities that lock the poor out of

profitable opportunities for self-advancement. From this perspective, in both countries, there are

three priorities.

First, rural infrastructure should have a high priority. China started its reform period with

very poor rural infrastructure. Fiscal and borrowing constraints meant that it was some 10 years

before it was feasible to embark on a massive expansion of infrastructure, such as the roads

program that started around 1990. The differences in rural infrastructure across counties have

strong explanatory power for subsequent consumption growth at the farm-household level in

27

rural China (Jalan and Ravallion, 2002). Quite reasonable rates of return are possible from well-

designed programs for developing infrastructure in poor rural areas (Ravallion and Chen, 2005b).

In India, the poor quality of rural infrastructure is widely acknowledged as an

impediment to growth and poverty reduction. It is believed that there are high returns in terms of

achieving more equitable growth from better rural finance and infrastructure in India, although

this is not simply a matter of building facilities but raises deeper issues about the need for

reforming existing institutional arrangements and provider incentives (World Bank, 2006).

Second, better policies are needed for delivering quality health and education services to

poor people. And third, policies are needed that allow key product and factor markets (for land,

labor and credit) to work better from the point of view of poor people. In India this includes

further deregulation of formal-sector labor markets. In the case of China, reducing the policy

impediments to migration, and legal reforms to allow a market in land-use rights in rural China,

giving farmers titles over land-use rights that they can sell, mortgage or pass onto their children,

are priorities. China has resisted embarking on agricultural land market reform. Neighboring

Vietnam did take this step in the 1990s, and the available evidence suggests that, on balance, this

reform has helped in reducing poverty (Ravallion and van de Walle, 2007).

Fiscal policy will play an important role in realizing these priorities. But much depends

on fiscal resource mobilization and exactly how that spending is done. Removing biases against

the poor in taxation and spending policies will be essential. As we have noted, reducing the

government’s implicit taxation of farmers through food grain procurement quotas has been a

powerful instrument against poverty in China. From this point of view, China’s recent policy to

give tax breaks to farmers in poor regions is surely welcome, although without alternative

revenue sources in poor areas one can expect either a decline in the local public investments and

services needed for poverty reduction, or further poorly-compensated expropriations of farm-

land by local authorities aiming to profit by selling the land to non-farm activities.

A second continuing issue in both countries is how to enhance local-level fiscal resources

in poor areas. The priority both countries now give to the decentralization of social spending will

have limited impacts on poverty and human development outcomes unless it comes with central

efforts to assure greater fiscal redistribution to poor regions from better-off—and increasingly

better-off—regions.

28

Recent initiatives

Policy makers and political leaders in both countries are clearly trying to find ways to

help the rural poor connect to the process of growth. In China, the reduction and progressive

elimination of agricultural taxes and fees and the limited introduction of subsidies for primary

education in poor counties during the latter half of the 10th Five-Year Plan were early

indications of a significant shift in the government’s priorities towards a greater emphasis on

improving welfare in the countryside.29 A key component of the plan is a package of measures

aimed at what the leadership has termed, “building a new socialist countryside”.30 The package

calls for a systemic elimination of all agricultural taxes. But the elimination of agricultural taxes

and fees raises new concerns. As we have noted, for many rural local governments, particularly

in interior provinces and in poorer areas, these taxes and fees have been the main source of

revenues from which to finance local public services, notably health and education. Not only

does this potentially jeopardize access to and the quality of health and education services, given

the huge disparities in the revenue bases of local governments, there are also implications for

consumption poverty. China has been relatively unique in the high savings rates found among

the poor. While a complete and detailed analysis of this question is yet to be done, common

sense and conventional wisdom suggest that (apart from any intrinsic cultural proclivities for

thrift) precautionary saving for uninsured health (and other) shocks, savings to finance

educational costs, and life-cycle savings to fund old-age living expenses all play an important

role in explaining why China’s poor save so much.

The government appears to be well-aware of this concern, and a large part of the

increased spending under the “building a new socialist countryside “ initiative is to be directed

towards education and health services in the countryside. Among the initiatives mentioned are

plans to provide several billion Yuan in supplementary funding for tens of millions of poor

primary and junior middle school students and to offer free nine-year compulsory education to

rural students. The central government also plans to double its subsidies for farmers if they join a

state-backed medical cooperative fund designed to reduce the financial burdens of farmers. Other

29 First announced at the meeting of the Central Party Committee of the Chinese Communist Party in October 2005, the reorientation in policy has been written in to the 11th Five Year Plan formally adopted by the National People’s Congress in its 2006 annual session. 30 According to the budget tabled at the National Peoples Congress, the Chinese government plans to spend 340 billion Yuan (US$42 billion) in agriculture, rural areas and farmers in 2006, which is 14 percent more than the previous year, and represents 22 percent of the increase in government spending from last year.

29

elements of the plan include increased subsidy payments for farmers and further government

investment in rural public works.

The “Minimum Livelihood Guarantee Scheme,” popularly known as Dibao has been the

Government of China’s main response to the new challenges of social protection in the more

market-based economy. This aims to guarantee a minimum income in urban areas, by filling the

gap between actual income and a “Dibao line” set locally. While in theory this would eliminate

Dibao poverty, the practice appears to fall well short of that goal due largely to imperfect

coverage of the target group (Chen et al., 2006). Geographic inequities in the program, stemming

from its heavily decentralized implementation, appear to have also reduced its impact (Ravallion,

2007). Reforming the program and expanding coverage—including to (risk-prone) rural areas—

pose a number of challenges.

If indeed these plans are implemented effectively and targeted to poorer areas and poorer

households in rural China, the prospects for poverty reduction, especially reduction in the

number of consumption poor, during the 11th Five-Year Plan look promising.

There have also been a number of new initiatives in India. The Rural Employment

Guarantee Act of 2005 guarantees 100 days of work a year at the minimum agricultural wage

rate to at least one member of every family. This is expected to have a large impact of rural

poverty, although it is far from obvious that the scheme is the most cost-effective option for this

purpose, once one considers all of the costs involved, including the forgone incomes of program

participants (Murgai and Ravallion, 2005).

The Government of India’s 2006-2007 budget also calls for substantially increased

spending on rural infrastructure, job creation, health and education. New programs include

Bharat Nirman (Building India) project to provide electricity, all-weather road connectivity and

safe drinking water to all of India’s villages and Sarva Siksha Abhiyan, which aims to assure a

minimum standard of elementary education. These programs are not explicitly targeted to poor

areas, but in all likelihood that will be the outcome given that villages lacking these services and

facilities will tend to be poor.

6. Conclusions

Aggregate economic growth is rarely balanced across regions or sectors of a developing

economy, and neither China nor India is an exception. We have seen that the post-reform pattern

30

of growth has not been particularly pro-poor in either country. In China, growth in the primary

sector (primarily agriculture) did more to reduce poverty and inequality than growth in either the

secondary or tertiary sectors. In India, with higher initial inequality in access to land than China,

agricultural growth was less important than tertiary sector growth. In both countries, there has

been a marked geographic unevenness in the growth process, with numerous lagging regions,

including some of those that started off among the poorest.

Income inequality is rising, although India has not yet experienced the same trend

increase in inequality that China has seen. Poverty in both countries is not becoming any more

responsive to aggregate economic growth and is becoming more responsive to rising inequality.

India’s poor did not start the reform period with the same advantages as China’s poor, in terms of

access to land and education. Persistent inequalities in human resource development and access

to essential infrastructure within both countries, but probably more so in India, are clearly

impeding the prospects for poor people to share in the aggregate economic gains spurred by

reforms. The geographic dimensions of their inequalities and the associated disparities in fiscal

resources and governmental capabilities loom large as policy concerns for the future in both

countries.

In the future, it will be harder for either country to maintain its past rate of progress

against poverty without addressing the problem of high and rising inequality. However, it is not

particularly useful to talk about “inequality” as a homogeneous entity in this context. Policy

needs to focus on the specific dimensions of inequality that create or preserve unequal

opportunities for participating in the gains from future economic growth. Arguably both

countries are seeing a rise in these bad inequalities over time as the good inequalities (conducive

to efficient growth) turn into bad ones, and the bad inequalities drive out the good ones.

While both countries need to be concerned about the “bad inequalities” we have pointed

to, we suspect that it is China where the near-term risk that rising inequality will jeopardize

growth is greater. Arguably, the Chinese authorities have been able to compensate for rising

inequality by achieving high growth rates; by this view, it is the rising inequality that fuels

growth in China, through the political economy of maintaining “social stability.” The “catch 22”

is that the emerging bad inequalities in China will make it harder to promote the growth that will

be needed to compensate for those inequalities. Maintaining sufficient growth will require even

greater efficacy of the policy levers used to promote growth.

31

Whether or not the problem of rising inequality is successfully addressed, there are likely

to be implications for the rest of the world. If the problem is not addressed, then there is a risk

that the high growth rates will not be maintained, with spillover effects for trade and growth

elsewhere. If it is addressed, and depending on exactly how this is done, there may be some

short-term costs to growth, although (as we have argued) redressing the bad inequalities would

actually be good for growth. There may also be consequences for the pattern of trade, such as

through a change in the sectoral composition of growth; for example, in both countries there

appears to be potential for cash crop expansion, which would attenuate one important source of

concern about rising inequality, and it can be expected that a non-negligible share of this

expansion in domestic cash-crop output would be exported.

The new initiatives underway in both countries are probably steps in the right direction,

although continuous evaluative research will be needed on the efficacy of these approaches

relative to alternatives. There are important but poorly resolved issues concerning the appropriate

balance between types of interventions. But an even harder challenge remains, namely to

improve governance—capacity, accountability and responsiveness—notably (but not only) at the

local level. If this challenge is left unmet, the ultimate efficacy of any of these initiatives will be

in doubt.

32

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Figure 1: Growth and poverty reduction in China and India, 1981–2004

China per-capita GDP

India per-capita GDP

China poverty rate

Indiapoverty rate

0

200

400

600

800

1000

1200

1400

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

Per

-cap

ita

GD

P (

con

stan

t 20

00 U

SD

)

0

10

20

30

40

50

60

70

% o

f p

op

ula

tio

n p

oo

r (b

elo

w $

-a-

day

)

Source Poverty measures from Chen and Ravallion (2007).

Figure 2: Growth rates at the sub-national level in China and India

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

Per-capita GDP of province(state) in 1978(1980)relative to poorest province(state)

An

nu

al g

row

th r

ate

(%)

of

per

-cap

ita

stat

e G

DP

bet

wee

n 1

978/

1980

an

d 2

004

Indian states Chinese provinces

Source: China Statistical Yearbook (various years); Central Survey Organization, Government of India

39

Figure 3: Sectoral GDP growth rates in China and India, 1980–2005

India

China

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

Agriculture Industry Services Agriculture Industry Services

Ave

rag

e an

nu

al g

row

th r

ate

(%)

1980-1985 1985-1990 1990-1995 1995-2000 2000-2005

Source: China Statistical Yearbook (various years); Central Survey Organization, Government of India

Figure 4: Growth incidence curves for China and India

0

1

2

3

4

5

6

7

8

9

10

0 10 20 30 40 50 60 70 80 90

The poorest p% of population ranked by per capita income/expenditure

An

nu

al g

row

th in

inco

me/

exp

end

itu

re p

er p

erso

n (

%)

Median

China (income) 1993-2004

India (expenditure)1993/1994-2004/2005

Source: Authors’ calculations. These are updated versions of the growth incidence curves for China (using household income) found in Ravallion and Chen (2003) and Ravallion (2004b) for India (household expenditure on consumption).

40

Figure 5: Trends in income inequality for China and India

China (income)

India (consumption)

25.0

30.0

35.0

40.0

45.0

1978 1983 1988 1993 1998 2003

Gin

i co

effi

cien

t o

f in

equ

alit

y

Source: Authors’ calculations for India and Ravallion and Chen (2007) for China.

Figure 6: Growth rates at the sub-national level in China and India

0

1

2

3

4

5

6

7

0 10 20 30 40 50 60

Initial poverty rate (%)

Ave

rag

e an

nu

al in

com

e g

row

th (

%)

China provinces India states (rural) India states (urban)

Source: China Statistical Yearbook (various years); Central Survey Organization, Government of India

41

Table 1: Poverty reduction and the sectoral composition of growth: China and India China India

–2.60 n.a. n.a. –0.99 n.a. Growth rate of GDP per capita (–2.16) (–3.38)

n.a. –8.07 –7.85 n.a. –1.16 Primary (share-weighted) (–3.97) (–4.09) (–2.96)

n.a. –1.75 n.a. n.a. 3.41 Secondary (share-weighted) (–1.21) (1.84)

n.a. –3.08 n.a. n.a. –3.42 Tertiary (share-weighted) (–1.24) (–2.74)

n.a. n.a. –2.25 n.a. n.a. Secondary + tertiary (–2.20)

R2 0.21 0.43 0.42 0.75 Source: Ravallion and Chen (2007) for China (1981–2001) and Ravallion and Datt (1996) for India (1951–1991). Note: t-ratios in parentheses. Table 2: Poverty reduction and the urban–rural composition of growth China India

–2.56 –1.46 Growth rate of mean rural income (share-weighted) (–8.43) (12.64)

0.09 –0.55 Growth rate of mean urban income (share-weighted) (0.20) (–1.37) 0.74 –4.46 Population shift effect

(0.16) (–1.31) R2 0.82 0.90

Note: t-ratios in parentheses. Source: Ravallion and Datt (1996) (for India) and Ravallion and Chen (2007) (for China).